Set YAML all uppercase in docs (#1280)

Author: RobertLucian

docs/deployments/batch-api.md

@@ -27,7 +27,7 @@ A Batch API deployed in Cortex will create/support the following:
 You specify the following:
 
 * a Cortex Predictor class in Python that defines how to initialize your model run batch inference
-* an API configuration yaml file that defines how your API will behave in production (parallelism, networking, compute, etc.)
+* an API configuration YAML file that defines how your API will behave in production (parallelism, networking, compute, etc.)
 
 Once you've implemented your predictor and defined your API configuration, you can use the Cortex CLI to deploy a Batch API. The Cortex CLI will package your predictor implementation and the rest of the code and dependencies and upload it to the Cortex Cluster. The Cortex Cluster will setup an endpoint to a web service that can receive job submission requests and manage jobs.
 

docs/deployments/batch-api/api-configuration.md

@@ -2,7 +2,7 @@
 
 _WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
 
-Once your model is [exported](../../guides/exporting.md) and you've implemented a [Predictor](predictors.md), you can configure your API via a yaml file (typically named `cortex.yaml`).
+Once your model is [exported](../../guides/exporting.md) and you've implemented a [Predictor](predictors.md), you can configure your API via a YAML file (typically named `cortex.yaml`).
 
 Reference the section below which corresponds to your Predictor type: [Python](#python-predictor), [TensorFlow](#tensorflow-predictor), or [ONNX](#onnx-predictor).
 

docs/deployments/batch-api/endpoints.md

@@ -177,7 +177,7 @@ RESPONSE:
         "start_time": <string>           # e.g. 2020-07-16T14:56:10.276007415Z
         "end_time": <string> (optional)  # e.g. 2020-07-16T14:56:10.276007415Z (only present if the job has completed)
     },
-    "api_spec": <string>,  # a base64 encoded string of your api configuration yaml that has been encoded in msgpack
+    "api_spec": <string>,  # a base64 encoded string of your api configuration YAML that has been encoded in msgpack
     "endpoint": <string>   # endpoint for this job
 }
 ```

docs/deployments/realtime-api.md

@@ -30,7 +30,7 @@ A Realtime API deployed in Cortex has the following features:
 You specify the following:
 
 * a Cortex Predictor class in Python that defines how to initialize and serve your model
-* an API configuration yaml file that defines how your API will behave in production (autoscaling, monitoring, networking, compute, etc.)
+* an API configuration YAML file that defines how your API will behave in production (autoscaling, monitoring, networking, compute, etc.)
 
 Once you've implemented your predictor and defined your API configuration, you can use the Cortex CLI to deploy a Realtime API. The Cortex CLI will package your predictor implementation and the rest of the code and dependencies and upload it to the Cortex Cluster. The Cortex Cluster will set up an HTTP endpoint that routes traffic to multiple replicas/copies of web servers initialized with your code.
 

docs/deployments/realtime-api/api-configuration.md

@@ -2,7 +2,7 @@
 
 _WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
 
-Once your model is [exported](../../guides/exporting.md) and you've implemented a [Predictor](predictors.md), you can configure your API via a yaml file (typically named `cortex.yaml`).
+Once your model is [exported](../../guides/exporting.md) and you've implemented a [Predictor](predictors.md), you can configure your API via a YAML file (typically named `cortex.yaml`).
 
 Reference the section below which corresponds to your Predictor type: [Python](#python-predictor), [TensorFlow](#tensorflow-predictor), or [ONNX](#onnx-predictor).